Pub. online:1 Jan 2013Type:Research ArticleOpen Access
Volume 24, Issue 1 (2013), pp. 119–152
Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.
Pub. online:1 Jan 2008Type:Research ArticleOpen Access
Volume 19, Issue 3 (2008), pp. 321–344
The purpose of the research described in this paper is to propose a framework and supporting tools that will help software companies to establish formalised methods that will be technically and socially sound with their needs. Following the framework the companies can asses and improve their existing ways of working, capture them into formalised methods and continuously enrich them based on the past development experiences. Furthermore, the formalised methods that are designed based on the suggested framework are flexible and can be automatically adjusted by the supporting tools to suite circumstances of a particular project or team. This paper describes the framework philosophy and its tool support.